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A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative Vitreoretinopathy.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § Methods › Evaluation of a Target Protein–SNAIL1 › RFdiffusion-Based Binder Design ↔ rf/examples/diffusion.ipynb, lines 475–514 · score 0.67 · initial_guess, rm aa, ProteinMPNN, soluble, folded, binder
  2. [2] § Methods › Evaluation of a Target Protein–SNAIL1 › RFdiffusion-Based Binder Design ↔ af/examples/RSO.ipynb, lines 257–320 · score 0.60 · initial guess, rm aa, Cysteine, soluble, backbone, sequences
  3. [3] § Methods › Evaluation of a Target Protein–SNAIL1 › RFdiffusion-Based Binder Design ↔ af/examples/RSO.ipynb, lines 767–801 · score 0.55 · multimer model, generated sequences, recycles, RMSD, template, pLDDT
  4. [4] § Results › RFdiffusion-Based Binder Design for SNAIL1 ↔ af/examples/RSO.ipynb, lines 121–231 · score 0.53 · designed sequence, ProteinMPNN sequence, AlphaFold, confidence, pLDDT, score

Paper

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The authors' code

Jupyter notebook · 1,098 lines · 56 KB · other · 3 matches

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Overview

Authors: Mak B Djulbegovic1, Nedym Hadzijahic2, David J Taylor Gonzalez3, Michael Antonietti4, Sidra Zafar1,5, Ajay E Kuriyan1,2
ORCID iDs: Mak B Djulbegovic, Nedym Hadzijahic, Ajay E Kuriyan
  1. Wills Eye Hospital, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
  2. University of Miami, Miami, Florida
  3. Department of Ophthalmology, Broward Health North, Pompano Beach, Florida
  4. Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts
  5. Mid Atlantic Retina at Wills Eye Hospital, Philadelphia, Pennsylvania
Institutions: Thomas Jefferson University Hospital (United States); Wills Eye Hospital (United States); University of Miami (United States); Broward Health (United States); Massachusetts Eye and Ear Infirmary (United States); Harvard University (United States); Mid Atlantic Retina (United States)
Journal: Ophthalmology science, volume 6, issue 8, article 101249
Dates: received 2 February 2026; accepted 18 May 2026; published online 25 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.xops.2026.101249 · PMID 42421755 · PMCID PMC13343151 · OpenAlex W7162299899
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Keywords: Proliferative vitreoretinopathy, Epithelial–mesenchymal transition, Intrinsic disorder, SNAIL1, Artificial intelligence
Topic: Retinal Imaging and Analysis (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: VitreoRetinal Surgery Foundation
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Objective: Proliferative vitreoretinopathy (PVR) remains a major cause of failure after rhegmatogenous retinal detachment repair and lacks effective pharmacologic therapies. Although epithelial–mesenchymal transition (EMT) is central to PVR pathogenesis, the structural determinants governing the tractability of EMT regulators, particularly those involving intrinsic disorder, remain poorly defined. We developed a disorder-aware, artificial intelligence–enabled computational framework to evaluate EMT-associated proteins in PVR and prioritize structurally tractable regulators for structure-based targeting.

Design: A computational, hypothesis-generating study employing an in silico screening and structural modeling pipeline.

Subjects: No human subjects or biological specimens were included. The dataset comprised 25 EMT-associated proteins implicated in PVR, curated through a narrative review of peer-reviewed literature.

Methods: Candidate proteins were evaluated using a multistage pipeline integrating intrinsic disorder profiling (Rapid Intrinsic Disorder Analysis Online), redox-sensitive disorder-to-order transition (DOT) analysis (AIUPred), and protein–protein interaction network assessment (Search Tool for the Retrieval of Interacting Genes/Proteins [STRING]). Structure-based modeling and generative binder design were then applied to the top-ranked candidate using RFdiffusion for de novo backbone generation, protein message passing neural network for sequence design, and AlphaFold2 for structural validation.

Main Outcome Measures: Primary measures were the proportion of intrinsically disordered residues, redox-sensitive disorder change, STRING network coherence within EMT-related pathways, and the structural consistency of the designed binder–target complex, assessed by root mean square deviation (RMSD) and mean per-residue confidence (predicted local distance difference test [pLDDT]).

Results: Of the 25 EMT-associated proteins screened, several exhibited intermediate intrinsic disorder profiles and measurable DOT potential. Snail Family Transcriptional Repressor 1 (SNAIL1) emerged as the highest-priority candidate, demonstrating an intermediate intrinsic disorder profile (∼35%), a pronounced redox-sensitive DOT region, and selective connectivity within EMT-related signaling networks. Functional mapping of the SNAIL1 C-terminal DOT segment identified 6 basic residues with literature-supported or motif-based regulatory significance (K187, R191, R224, K234, K253, and R264). Following sequence design and structural validation, the top-ranked binder exhibited the lowest structural deviation within the generated ensemble (RMSD 18.5 Å) and high per-residue confidence (mean pLDDT 0.84).

Conclusions: Our study introduces a disorder-informed computational framework for prioritizing structurally tractable EMT regulators in PVR. As a proof-of-concept, the pipeline nominates SNAIL1 and generates a structure-aware de novo binder targeting its C-terminal DOT region, providing a foundation for disorder-based therapeutic discovery in fibrotic retinal disease.

Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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sokrypton/colabdesign

License: other
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e31a56fe1d9b4de25c8697f3a28b75892941cc72, 23 October 2025
Languages: Python (95), Jupyter (28), JavaScript (1)
Size: 155 files, 124 scripts
Software Heritage: not archived
Found in: the text, “RFdiffusion-Based Binder Design”
Holds: README, license file, environment (setup.py), continuous integration, 28 notebooks
Not found: CITATION.cff, tests, documentation
Tools: NumPy (77 files), JAX (71 files), Matplotlib (14 files), SciPy (10 files), pandas (7 files), Biopython (5 files), PyTorch (5 files), Plotly (2 files), Hugging Face Transformers (2 files), Keras (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
126 files, not copied: shown from their source

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At the source: colab.research.google.com/github/sokrypton/ColabDesign/blob/main/rf/examples/diffusion_ori.ipynb

Tracing map

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  • 124 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Version 2, 28 September 2026

  • Authors: added Nedym Hadzijahic (0009-0004-1454-2705); removed Nedym Hadzijahic

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 1 funder, 62 references.

Cite

This paper

Djulbegovic, M. B., Hadzijahic, N., Taylor Gonzalez, D. J., Antonietti, M., Zafar, S., & Kuriyan, A. E. (2026). A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative Vitreoretinopathy. Ophthalmology science, 6(8), 101249. https://doi.org/10.1016/j.xops.2026.101249

BibTeX

@article{djulbegovic2026disorder,
author = {Djulbegovic, Mak B and Hadzijahic, Nedym and Taylor Gonzalez, David J and Antonietti, Michael and Zafar, Sidra and Kuriyan, Ajay E},
title = {{A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative Vitreoretinopathy}},
journal = {Ophthalmology science},
year = {2026},
month = may,
volume = {6},
number = {8},
pages = {101249},
publisher = {Elsevier},
issn = {2666-9145},
doi = {10.1016/j.xops.2026.101249},
url = {https://doi.org/10.1016/j.xops.2026.101249},
pmid = {42421755},
pmcid = {PMC13343151}
}

RIS

TY - JOUR
AU - Djulbegovic, Mak B
AU - Hadzijahic, Nedym
AU - Taylor Gonzalez, David J
AU - Antonietti, Michael
AU - Zafar, Sidra
AU - Kuriyan, Ajay E
TI - A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative Vitreoretinopathy
T2 - Ophthalmology science
J2 - Ophthalmol Sci
PY - 2026
DA - 2026/05/25
VL - 6
IS - 8
SP - 101249
SN - 2666-9145
PB - Elsevier
DO - 10.1016/j.xops.2026.101249
UR - https://doi.org/10.1016/j.xops.2026.101249
LA - en
ER -

CSL-JSON

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